Akademik Veri Yönetim Sistemi
2024 Kardiyovasküler Akademi Derneği Kongresi, Girne, Kıbrıs (Kktc), 17 - 21 Eylül 2024, ss.12, (Özet Bildiri)
Development
and Internal/External Validation of a Prediction Model for Premature Ventricular
Contractions Unresponsive to Medical Treatment
Abstract:
Background: Ventricular
premature contractions (PVCs) are the most common ventricular arrhythmias.
However, to date, no risk stratification tool exists to assess response to
medical therapy in patients with frequent PVCs (>5% per 24 hours). We aimed
to develop and validate a clinical prediction model evaluating response to
medical therapy in frequent PVCs.
Methods: We conducted a
prospective cohort study of patients with frequent PVCs who were considered for
medical treatment. The
study outcome was unresponsiveness to
the medical treatment (24-hour PVC count not diminishing by at
least 80%) in patients with frequent PVCs at 3-6 months follow-up. Potential
predictors were, age (years), PVC burden (%), LVEF % (<55, ³55), PVC QRS width (msec), mean heart rate
(beat/min), sinus beat QTc (msec), PVC coupling interval, gender, presence of
multifocal PVC and Non-sustained VT. Binary logistic regression analyses were
performed to develop and internally validate the model. Finally, we used
internal-external cross validation using leave-one-out center.
Results: 1644 patients were
included in the study (mean
age 52.2±13.5 years and 56.3% male). The
frequency of unresponsiveness to the medical treatment in patients with
frequent PVC was 31.2% (n=513). In the
model, PVC burden (%), LVEF% (<55, ³55) and PVC QRS width were found to be the three strongest predictors. The
apparent and internal validation discriminations of the model (C-statistics in
internal validation 0.910) were quite satisfactory. Model discriminations and
calibration metrics with internal-external cross validation were similar to the
apparent model and were deemed acceptable (https://demonoreflow.shinyapps.io/dynnomapp/ ).
Conclusions: We
developed and validated a model that accurately predicts unresponsiveness to medical
treatment in patients with frequent PVC.
Our model, once externally validated, has the potential to facilitate
management decisions by providing individualized risk estimates in patients
with frequent PVC for whom ablation may be suggested as a first line treatment
modality.